Senior AI Application Engineer

We are seeking an Sr. AI Application Engineer to develop and deploy advanced AI systems that bridge data-driven intelligence with real-world engineering. The ideal candidate will have a strong foundation in machine learning , agentic AI , and physics-informed modeling , and will apply these techniques to complex engineering problems such as multi-objective design optimization , AI model fine-tuning , simulation acceleration , and autonomous decision-making systems . This role requires both theoretical depth in AI/ML algorithms and practical engineering insight to create robust, deployable solutions in domains such as mechanical, electrical, aerospace, or materials engineering. Key Responsibilities AI Model Development & Optimization Design, train, and fine-tune machine learning and deep learning models for engineering applications. Implement physics-informed neural networks (PINNs) , surrogate modeling, or hybrid AI-physics systems. Develop and apply multi-objective optimization algorithms (e.g., genetic algorithms, reinforcement learning, Bayesian optimization). Agentic AI & Autonomous Systems Build and integrate AI agents capable of autonomous reasoning, decision-making, and goal-directed task execution in engineering workflows. Employ agentic architectures (e.g., multi-agent systems, LLM-based reasoning agents) for simulation orchestration and design automation. Apply reinforcement learning or control-oriented AI for adaptive engineering systems. AI Deployment & Integration Implement scalable inference pipelines for real-time or batch processing in production environments (cloud or edge). Collaborate with DevOps/MLOps teams to deploy AI models using frameworks like TensorFlow Serving, TorchServe, ONNX, or NVIDIA Triton. Integrate AI systems into engineering tools and environments (e.g., CAD/CAE software, simulation platforms, or digital twins). Research & Innovation Explore novel applications of physics-based AI , agentic reasoning , and self-improving systems within engineering contexts. Stay current with advancements in foundation models , multi-modal AI , and generative design . Contribute to white papers, patents, and internal R&D initiatives. Collaboration & Communication Work closely with domain engineers, data scientists, and software developers to translate engineering challenges into AI-driven solutions. Communicate complex technical concepts to multidisciplinary teams and stakeholders. Required Qualifications Education: M.S. degree with 5+ years of industry experience, or Ph.D. degree with 3+ years of relevant experience, in Artificial Intelligence, Computer Science, Electrical or Mechanical Engineering, Applied Physics, or a related field. Technical Expertise: Strong proficiency in Python , and familiarity with PyTorch , TensorFlow , or JAX . Experience with machine learning algorithms , deep learning architectures , and optimization methods . Solid background in numerical methods , physics simulation , or computational modeling . Familiarity with agent frameworks (e.g., LangChain, AutoGen, CrewAI, OpenDevin, or custom LLM orchestration systems). Proficiency in MLOps , data pipelines , and AI deployment on cloud or edge infrastructure (AWS, Azure, GCP). Soft Skills: Strong analytical thinking, problem-solving, and communication skills. Ability to work in multidisciplinary teams and adapt to evolving technologies. Preferred Qualifications Experience with reinforcement learning , evolutionary algorithms , or active learning for design optimization. Background in multi-physics simulation (CFD, FEA, thermal, or electromagnetic). Prior exposure to generative AI for engineering design or AI-driven control systems . Familiarity with digital twins and real-time sensor integration . Publications or patents in AI for engineering or computational sciences.